This application discloses a method of pattern recognition, and in particular of character and word recognition for handwriting cursive, hand printing, machine print and digital images.
To date, handwriting and machine print recognition technologies have had accuracy limitations due to computer hardware constraints that forced run-time code into very small footprints. When a new print style, font style, or handwriting style or anomaly was encountered, the recognition source code typically had to be re-written, re-compiled, and re-distributed to users. Even today's institutions of higher learning promote curricula, masters and doctoral degrees, and related research and development departments based upon small footprint practices.
Today there is a vast amount of computer RAM, storage, and parallel processing power available. Indications are the future will continue to bring even greater parallel computing hardware capabilities at lower cost. The blade server industry may soon produce a single blade or single circuit board containing one thousand floating point processors. Unfortunately, methods taking advantage of this and greater levels of distributed and massive parallel processing, caching and storage speed/capacity capabilities to perform Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR) and Intelligent Word Recognition and Intelligent Handwriting Recognition have not been developed. Furthermore, being able to adapt to new character and word anomalies, font styles, and attributes without re-writing of the underlying source code would also be a useful advance over prior technologies.